[ { "id": "marin-thesis", "type_id": "phdthesis", "tu_id": null, "repositum_id": "20.500.12708/215385", "title": "Proximity-Based Point Cloud Reconstruction", "date": "2025-02", "abstract": "Extrapolating information from incomplete data is a key human skill, enabling us to inferpatterns and make predictions from limited observations. A prime example is our ability to perceive coherent shapes from seemingly random point sets, a key aspect of cognition.However, data reconstruction becomes challenging when no predefined rules exist, as it is unclear how to connect the data or infer patterns. In computer graphics, a major goal isto replicate this human ability by developing algorithms that can accurately reconstruct original structures or extract meaningful information from raw, disconnected data.The contributions of this thesis deal with point cloud reconstruction, leveraging proximity-based methods, with a particular focus on a specific proximity-encoding data structure -the spheres-of-influence graph (SIG). We discuss curve reconstruction, where we automate the game of connecting the dots to create contours, providing theoretical guarantees for our method. We obtain the best results compared to similar methods for manifold curves. We extend our curve reconstruction to manifolds, overcoming the challenges of moving to different domains, and extending our theoreticalguarantees. We are able to reconstruct curves from sparser inputs compared to the state-of-the-art, and we explorevarious settings in which these curves can live. We investigate the properties of the SIGas a parameter-free proximity encoding structure of three-dimensional point clouds. We introduce new spatial bounds for the SIG neighbors as a theoretical contribution. We analyze how close the encoding is to the ground truth surface compared to the commonly used kNN graphs, and we evaluate our performance in the context of normal estimationas an application. Lastly, we introduce SING – a stability-incorporated neighborhood graph, a useful tool with various applications, such as clustering, and with a strong theoretical background in topological data analysis.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "Teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2012, "image_height": 1035, "name": "marin-thesis-Teaser.png", "type": "image/png", "size": 599974, "path": "Publication:marin-thesis", "url": "https://www.cg.tuwien.ac.at/research/publications/2025/marin-thesis/marin-thesis-Teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2025/marin-thesis/marin-thesis-Teaser:thumb{{size}}.png" }, "sync_repositum_override": "access,date", "repositum_presentation_id": null, "authors": [ { "id": 1848, "drupal_id": 3143, "orcid_id": "0000-0002-8812-9719", "website": 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"Rainer_2017", "rait_alexius-2021-baa", "Rait2022", "rasch_martina-2013-HDRImage", "rasoulzadeh-2022-strokes2surface", "rasoulzadeh-2024-strokes2surface", "RAUCH-2011-TMS", "recheis-2009-arr", "REICHHOLF-2023-HRO", "Reichinger-12", "reiher_joerg-2023-pra", "reiher-joerg-2024-daa", "Reimerth_Saskia_2016_RGU", "REINALTER_2010_VIS", "reinwald-2017-baa", "reisinger-2023-iad", "Reisner_2013_RSB", "Reisner_Irene_2013_R3D", "reitsamer-2008-tsud", "reznicek-2020-fpgaray", "richter-2020-ani", "riegler_2020_framework", "roch-2007-rtt", "Roch2006", "Roegner_RHF_2010", "ROEGNER-2014-TUI", "ROEGNER-2015-IBR", "ROESSLER-2012-OGLES", "roth_2021_vdst", "roth_vdi", "roth-2023-vlk", "rozanics-lsf", "rubik_michael-2022-baa", "Rudolf_06_IDM", "rumpelnik_martin_2020_PRM", "rumpelnik_martin_2020_PRM", "Rumpelnik_Martin-2020-NPR", "rumpelnik-2022-vlk", "rumpler_2019_PPC", "Rumpler-2019-PPC", "RUMPLER-2021-VOGL", "rumpler-2023-dcmcm", "S05", "SABADELLO-2006-HEG", "sakai_2013", "SAKAI-2015-EAPI", 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"TR-186-2-08-09", "TR-186-2-12-01", "Tragust-2012-GMHPC", "Tragust-2014-master-thesis", "traxler-1997-TRA", "trenkwalder-2008-landscape", "trenkwalder-2013-ma", "trenovatz-lpd", "trumpf_stefan_2012", "Tucek_2018", "Tucek_Tom-2016-aai", "tumpach_barbara-pra", "ulschmid-2024-reo", "ulschmid-2025-apc", "ulschmid-phdthesis", "unterguggenberger-2010-cmph", "unterguggenberger-2020-fmvr", "unterguggenberger-2021-msh", "unterguggenberger-2022-vulkan", "unterguggenberger-2023-vaw", "unterguggenberger-2024-fropo", "unterguggenberger-phdthesis", "VASCO-2019-EVR", "vasiljevic-gpc", "VASILJEVS-2018-PMPL", "vis-foa", "VOELKL-2013-RTRS", "voglreiter-2023-tro", "Voglsam_2012_RTR", "Voglsam_2013_RRT", "wagner-2024-par", "wallner_2008_ltosd", "WALLNER-2010-CSSAO", "wallner-2015-InfoTheoryPlay", "wallner-2015-ModelingRoutinization", "Wang-2016-BAC", "wang-sifuentes-2018-cgbase", "weber_2008_sh", "WEBER-2012-MIC", "WEBER-2015-PRA", "WEBER-2015-PRA1", "weidlich_2009_REL", "weidlich-2009-dispersion", 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"WIMMER-2016-HARVEST4D", "WIMMER-2019-CGSG", "WIMMER-2020-ASG", "wimmer-2022-acd", "wimmer-2023-acd", "Win25", "WINKLER-2013-AMO", "Winkler-2017", "WINKLER-2019-PDG", "winklhofer_christoph-2013-RRMR", "winklmueller-aso", "withalm_brigitte-2022-baa", "Woerister_2012_ACS", "wolffhardt-2026-prj", "wonka-2000-VisP", "Wonka-2001-Ins", "Wonka-2001-IV", "Wonka-2003-Ins", "WONKA-2006-GVS", "x-mas-2013", "xmas-2025", "yoo-chaeran-2025-pr", "zapotocky_2013_ma", "ZAU14", "zeilinger_matthias-2020-baa", "Zezulka_Matthias-2023-MatLabOptInterface", "Zezulka-2024-FTS", "Zezulka-2025-NRL", "Zezulka-2026-FGTS", "Zezulka-2026-GridThermalSim", "zhang_le-2010-IGA", "zhang-thesis", "ZL_2011_IVIG", "zotti-2001-dip", "zotti-2003-ash", "zotti-2005-lum", "Zotti-2005-vis", "zotti-2006-dgm", "zotti-2006-pla", "zotti-2007-eg", "zotti-2007-hdr", "zotti-2007-PhD", "zotti-2007-wscg", "ZOTTI-2016-VAA", "ZOTTI-2017-BM", "ZOTTI-2017-TSP", "zsolnai-2018-gms", "zsolnai-feher-thesis-2019", "zsolnai-ist-invited-2014", "zsolnaifeher-2019-pme", "zsolnaifeher-2020-pme" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Workgroup&id=rend", "__class": "Workgroup" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2025/marin-thesis/", "__class": "Publication" }, { "id": "marin-2024-sing", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/208566", "title": "SING: Stability-Incorporated Neighborhood Graph", "date": "2024-12", "abstract": "We introduce the Stability-Incorporated Neighborhood Graph (SING), a novel density-aware structure designed to capture the intrinsic geometric properties of a point set. We improve upon the spheres-of-influence graph by incorporating additional features to offer more flexibility and control in encoding proximity information and capturing local density variations. Through persistence analysis on our proximity graph, we propose a new clustering technique and explore additional variants incorporating extra features for the proximity criterion. Alongside the detailed analysis and comparison to evaluate its performance on various datasets, our experiments demonstrate that the proposed method can effectively extract meaningful clusters from diverse datasets with variations in density and correlation. Our application scenarios underscore the advantages of the proposed graph over classical neighborhood graphs, particularly in terms of parameter tuning.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 2607, "image_height": 709, "name": "marin-2024-sing-image.png", "type": "image/png", "size": 897420, "path": "Publication:marin-2024-sing", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-sing/marin-2024-sing-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-sing/marin-2024-sing-image:thumb{{size}}.png" }, "sync_repositum_override": "date", "repositum_presentation_id": null, "authors": [ { "id": 1848, "drupal_id": 3143, "orcid_id": "0000-0002-8812-9719", "website": "https://www.cg.tuwien.ac.at/staff/DianaMarin", "firstname": "Diana", "lastname": "Marin", "searchname": "Diana Marin", "is_teacher": false, "name": "Diana Marin", "publications": [ "ehlers-2024-vgs", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "marin-thesis", "ohrhallinger_stefan-2024-inv" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "brunner_lukas_2022_ssb", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "Komon2022", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2022-sig", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2022-sigdt", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2023-pic", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2024-dsr", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2024-pcp", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2024-rcf", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2024-sing", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "mujadzic-2023-hfp", "field_id": "supervisor", "ordernum": 0 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1848", "__class": "Author" }, { "id": 1825, "drupal_id": null, "orcid_id": null, "website": null, "firstname": "Amal Dev", "lastname": "Parakkat", "searchname": "Amal Dev Parakkat", "is_teacher": false, "name": "Amal Dev Parakkat", "publications": [ "marin-2024-sing", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "parakkat-2024-ballmerge" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "cardoso-thesis", "field_id": "reviewer_1", "ordernum": 0 }, { "publ_id": "ohrhallinger-2021-egs", "field_id": "lecturer", "ordernum": 2 }, { "publ_id": "ohrhallinger-2022-e2t", "field_id": "lecturer", "ordernum": 1 } ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=Author&id=1825", "__class": "Author" }, { "id": 948, "drupal_id": 185, "orcid_id": "0000-0002-2526-7700", "website": "https://www.cg.tuwien.ac.at/staff/StefanOhrhallinger", "firstname": "Stefan", "lastname": "Ohrhallinger", "searchname": "Stefan Ohrhallinger", "is_teacher": false, "name": "Stefan Ohrhallinger", "publications": [ "erler-2020-p2s", "leimer_2020-cag", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "ohrhallinger_stefan-2011-001", "ohrhallinger_stefan-2012-mle", "ohrhallinger_stefan-2013-c2d", "ohrhallinger_stefan-2013-smi", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger_stefan-2019-xms", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2016-sgp", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", 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Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

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The design and editing of curves on surfaces has only recently begun to receive attention, primarily relying on human assistance, and where not, limited by very strict sampling conditions. In this work, we formally improve on the state-of-the-art requirements and introduce an innovative algorithm capable of reconstructing closed curves directly on surfaces from a given sparse set of sample points. We extend and adapt a state-of-the-art planar curve reconstruction method to the realm of surfaces while dealing with the challenges arising from working on non-Euclidean domains. We demonstrate the robustness of our method by reconstructing multiple curves on various surface meshes. 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"research_areas": [], "keywords": [ "CCS Concepts", "Graph algorithms", "Mesh geometry models", "Paths and connectivity problems" ], "weblinks": [], "files": [ { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "name": "marin-2024-rcf-image.png", "type": "image/png", "size": 706684, "path": "Publication:marin-2024-rcf", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-rcf/marin-2024-rcf-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-rcf/marin-2024-rcf-image:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "WorldScale", "workgroup_id": "rend", "drupal_id": 4158, "drupal_path": "/research/projects/WorldScale", "name": "Modeling the World at Scale", "name_de": "Modellierung der Welt nach Maß", "short_title": "WorldScale", "website": null, "status": "active", "short_abstract": "Vision: reconstruct a model of the world that permits online level-of-detail extraction.", "abstract": "

Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

\r\n", "start_date": "2020-05-01", "end_date": "2026-04-30", "leader_id": 948, "logo": { "name": "logo.jpg", "path": "project:WorldScale", "type": "image/jpeg", "size": 64953, "orig_name": "logo.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/WorldScale/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "WWTF", "contract_number": "ICT19-009", "comment": "€ 578.450" } ], "research_areas": [ "Geometry" ], "publications": [ "antes-2026-maa", "ausserer-2026-baa", "balvan-rsf", "beleznai_csaba-2025-pr", "belic_dejan_pr", "bodzenta-2026-baa", "csecsy-fcd", "darovskikh_leonid-2025-prj", "deak-a3", "depner_dennis-2020-baa", "depner_dennis-2022-maa", "elagrod_ahmed-baa", "fitzinger_andreas-daa", "fraissl_niklas-2023-pra", "fuchs_peter-2024-baa", "fuericht_2025_prj", "fuericht_elias-2025-prj", "hamzic_ademir-baa", "harisch-mvs", "hatzinger-2026-baa", "hoffer-toth-com", "holom_2025-prj", "jira_marcel-2024-baa", "kaipel_nikolas-2025-baa", "karampekios_anastasios-prj", "kazda-tmd", "knoetzl_alexander-2025-prj", "kohlhas_jakob-2023-pra", "konrad-2020-baa", "kozonits_marcel-2025-baa", "kralicek_sebastian_baa", "kubicek-2024-baa", "lang_jakob-2022-baa", "lazarevic-tci", "leutschacher_2025_prj", "macho_jonas-2024-baa", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "markovich_aleksandar-2022-maa", "mayr_patrick_maa", "micu_2025-prj", "miklautsch_florian-baa", "mueller-2024-rbt", "mujadzic_iman_baa", "mujadzic-2023-hfp", "nawrocki-2024-nsr", "negi_ashish-2023-baa", "neumann-sfd", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "pakkanen-2026-prj", "parakkat-2024-ballmerge", "pointner_simon-2022-maa", "proelss-bdp", "prohaska_julia-2023-baa", "reiher_joerg-2023-pra", "reiher-joerg-2024-daa", "rozanics-lsf", "rubik_michael-2022-baa", "schrammel_sebastian_baa", "schulz-faf", "seisenbacher-cyclesafely", "slajcho_levente-baa", "slavtchev-dcd", "starnberger-cli", "steinheber-2024-oto", "steinschauer-2020-baa", "trenovatz-lpd", "tumpach_barbara-pra", "vasiljevic-gpc", "wiesinger_2025-baa", "wiesinger_klemens-2023-baa", "wildt-tci", "withalm_brigitte-2022-baa", "wolffhardt-2026-prj", "yoo-chaeran-2025-pr", "zeilinger_matthias-2020-baa" ], "url": "https://www.cg.tuwien.ac.at/research/projects/WorldScale", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-rcf/", "__class": "Publication" }, { "id": "ohrhallinger_stefan-2024-inv", "type_id": "talk", "tu_id": null, "repositum_id": null, "title": "Sampling and reconstructing point clouds", "date": "2024-05-28", "abstract": "Curve and surface reconstruction from unstructured points represent a fundamental problem in computer graphics and computer vision, with many applications. The quest for better solutions for this ill-posed problem is riddled with various kinds of artifacts such as noise, outliers, and missing data.\n\nMoreover, the reconstruction problem usually implies further input requirements: how many samples do we need for a successful reconstruction, what properties should these samples satisfy and how can we obtain such sets. And once we obtain these point samples, how can we extract connectivity that best approximates the initial surface they have been sampled from?\n\nWe will discuss about various sampling strategies, corresponding reconstruction methods, with multiple applications in automating sketch coloring, adaptive meshing for faster simulations, and cultural heritage.\n", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 1310, "image_height": 596, "name": "ohrhallinger_stefan-2024-inv-image.png", "type": "image/png", "size": 762834, "path": "Publication:ohrhallinger_stefan-2024-inv", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/ohrhallinger_stefan-2024-inv/ohrhallinger_stefan-2024-inv-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/ohrhallinger_stefan-2024-inv/ohrhallinger_stefan-2024-inv-image:thumb{{size}}.png" }, "sync_repositum_override": null, "repositum_presentation_id": null, "authors": [ { "id": 948, "drupal_id": 185, "orcid_id": "0000-0002-2526-7700", "website": "https://www.cg.tuwien.ac.at/staff/StefanOhrhallinger", "firstname": "Stefan", "lastname": "Ohrhallinger", "searchname": "Stefan Ohrhallinger", "is_teacher": false, "name": "Stefan Ohrhallinger", "publications": [ "erler-2020-p2s", "leimer_2020-cag", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "ohrhallinger_stefan-2011-001", "ohrhallinger_stefan-2012-mle", "ohrhallinger_stefan-2013-c2d", "ohrhallinger_stefan-2013-smi", "ohrhallinger_stefan-2018-cgf", "ohrhallinger_stefan-2018-pg", "ohrhallinger_stefan-2019-xms", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", 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Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

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Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

\r\n", "start_date": "2020-05-01", "end_date": "2026-04-30", "leader_id": 948, "logo": { "name": "logo.jpg", "path": "project:WorldScale", "type": "image/jpeg", "size": 64953, "orig_name": "logo.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/WorldScale/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "WWTF", "contract_number": "ICT19-009", "comment": "€ 578.450" } ], "research_areas": [ "Geometry" ], "publications": [ "antes-2026-maa", "ausserer-2026-baa", "balvan-rsf", "beleznai_csaba-2025-pr", "belic_dejan_pr", "bodzenta-2026-baa", "csecsy-fcd", "darovskikh_leonid-2025-prj", "deak-a3", "depner_dennis-2020-baa", "depner_dennis-2022-maa", "elagrod_ahmed-baa", "fitzinger_andreas-daa", "fraissl_niklas-2023-pra", "fuchs_peter-2024-baa", "fuericht_2025_prj", "fuericht_elias-2025-prj", "hamzic_ademir-baa", "harisch-mvs", "hatzinger-2026-baa", "hoffer-toth-com", "holom_2025-prj", "jira_marcel-2024-baa", "kaipel_nikolas-2025-baa", "karampekios_anastasios-prj", "kazda-tmd", "knoetzl_alexander-2025-prj", "kohlhas_jakob-2023-pra", "konrad-2020-baa", "kozonits_marcel-2025-baa", "kralicek_sebastian_baa", "kubicek-2024-baa", "lang_jakob-2022-baa", "lazarevic-tci", "leutschacher_2025_prj", "macho_jonas-2024-baa", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "markovich_aleksandar-2022-maa", "mayr_patrick_maa", "micu_2025-prj", "miklautsch_florian-baa", "mueller-2024-rbt", "mujadzic_iman_baa", "mujadzic-2023-hfp", "nawrocki-2024-nsr", "negi_ashish-2023-baa", "neumann-sfd", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "pakkanen-2026-prj", "parakkat-2024-ballmerge", "pointner_simon-2022-maa", "proelss-bdp", "prohaska_julia-2023-baa", "reiher_joerg-2023-pra", "reiher-joerg-2024-daa", "rozanics-lsf", "rubik_michael-2022-baa", "schrammel_sebastian_baa", "schulz-faf", "seisenbacher-cyclesafely", "slajcho_levente-baa", "slavtchev-dcd", "starnberger-cli", "steinheber-2024-oto", "steinschauer-2020-baa", "trenovatz-lpd", "tumpach_barbara-pra", "vasiljevic-gpc", "wiesinger_2025-baa", "wiesinger_klemens-2023-baa", "wildt-tci", "withalm_brigitte-2022-baa", "wolffhardt-2026-prj", "yoo-chaeran-2025-pr", "zeilinger_matthias-2020-baa" ], "url": "https://www.cg.tuwien.ac.at/research/projects/WorldScale", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-dsr/", "__class": "Publication" }, { "id": "ehlers-2024-vgs", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/196069", "title": "Visualizing Group Structure in Compound Graphs: The Current State, Lessons Learned, and Outstanding Opportunities", "date": "2024-03-11", "abstract": "Compound graphs are common across domains, from social science to biochemical pathway studies, and their visualization is important to both their exploration and analysis. However, effectively visualizing a compound graph's topology and group structure requires careful consideration, as evident by the many different approaches to this particular problem. To better understand the current advancements in compound graph visualization, we have consolidated and streamlined existing surveys' taxonomies. More specifically, we aim to disentangle the visual relationship between graph topology and group structure from the visual encoding used to visualize its group structure in order to identify interesting gaps in the literature. In so doing, we are able to enumerate a number of lessons learned and gain a better understanding of the outstanding research opportunities and practical implications across domains.", "authors_et_al": false, "substitute": null, "main_image": null, "sync_repositum_override": "projects", "repositum_presentation_id": null, "authors": [ { "id": 1850, "drupal_id": 3267, "orcid_id": "0000-0002-5994-1492", "website": "https://www.cg.tuwien.ac.at/staff/HenryEhlers", "firstname": "Henry", "lastname": "Ehlers", "searchname": "Henry Ehlers", "is_teacher": false, "name": "Henry Ehlers", "publications": [ "Ehlers_PhD", "ehlers-2023-iro", "ehlers-2024-mmm", "ehlers-2024-vgs", "ehlers-2025-ait", "ehlers-2025-battlegraphs", "ehlers-2025-penta", "ehlers-2025-www", "musleh-2024-conan", "oda-2025-artevoviewer", "pahr-2024-ieo", "pahr-2025-holographs", "pahr-2025-nodkant", "tanaka-2024-vor" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "ehlers-2024-vgs", 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", "abstract": "

Data and information created by various processes (e.g., simulations) is getting larger, and more and more complex. We use interactive visualization and analysis techniques to facilitate exploration of complex parameter spaces (e.g., to analyze and compare parameters for automotive engineering, reconstruction algorithms, or geometry generators) or unstructured information collections (e.g., annotated digital documents or image collections). Our research focus lies on novel visual encodings and interaction techniques to explore a large amount of abstract data, often in combination with analytical reasoning.

\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/InfoVis", "teaser": { "name": "teaser.png", "path": "research_area:InfoVis", "type": "image/png", "size": 60875, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/InfoVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "8564188", "arbesser_ba_2023", "Arleo-2019-vis", "Batik-2021", "batik-2021-gd", "bayat-2022-mva", "beham-2014-cupid", "bhore-2021-issac", "birsak-2014-agtb", "birsak-2017-dpe", "borgo-2013-gly", "borondy2022", "Braunsperger-2025_bsc", "Bruckner_Stefan_2015_VAS", "bugnar-esk-2021", "byska-2019-mdfc", "Cai_2018", "casellato-2016-pkg", "Chen-Information-2016", "cizmic-2018-evd", "cmolik-2020-tvcg", "Cornel_2020", "daae-lampe-2010-dif", "Deutsch-2018", "Diehl_2015", "Diehl_2021", "dworschak-2016-szcm", "Eckelt_2018_01", "edlinger-2018-vwr", "ehlers-2024-vgs", "eichner-2024-erv", "eitler-2024-sos", "eiweck-hnv-2021", "ellhotka-2025-ipo", "eschner-2023-evl", "eschner-2025-ide", "Escribano_2017", "fourousan2021", "furmanova_2020", "Gadllah_Hani_2016", "Gall2020", "Ganuza_ML_2015_ISA", "georgiev-2019-cbg", "geyer_lukas_2015_prj", "geymayer-2017-std", "Groeller_2011_CW", "Groeller_2011_NR", "Groeller_2014_RWA", "Groeller_2016_P1", "Groeller_2016_P2", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2021-10", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V5_2020", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "gundacker-2020-wlm", "gusenbauer-2018", "gusenbauer-2018-P", "Heim_2021", "heim-2024-accustripes", "heim-gall-2020-dde", "Heinzl2021", "holzer-edo", "hromniak-2019-vcn", "huber-2025-eed", "Iijima-2020-iV", "indirectBiasLanguageModels-2023", "irendorfer-2024-uat", "karnik-09-shapegrammar", "Kehrer-2010-mom", "kehrer-2012-mod", "kehrer-2013-SBC", "Kehrer-2013-STAR", "klaffenboeck-2024-rva", "klaffenboeck-thesis", "Klein_Tobias_2015TIV", "klein-2016-WCL", "knoetzl_alexander-2025-prj", "koeppel-2021", "Kolter_2021", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch-2021-lov", "Kuroko-2020-iV", "Laschober-2018", "li-2019-gdc", "lippeck-2022-mna", "louis-alexandre_dit_petit-frere-2022-veo", "magg2022", "mahler-2021-mdar", "martorell2022", "maruyama-2019-iv", "Matkovic_Kresimir_2015_", "Matkovic-2014-ieee", "matt-2024-cvil", "matt-2025-scv", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "medeiros-2023-gwf", "meka-2024-lpi", "melcher-2024-asi", "Miao_2015_VCBM", "miao_2016_cgf", "Miao_Haichao_2015_VQC", "miao2018Dimsum", "mizuno-2019-eurovis", "Muehlbacher_diss_2018", "musleh_maath_2021_mam", "musleh_maath-2021-mam1", "musleh_maath-2021-mam2", "Musleh_PhD", "musleh-2022-mam5", "musleh-2025-trustme", "Neubauer2020", "Nowak_2021", "ortner-2016-tunnel", "ortner-2016-visaware", "pahr-2024-ieo", "PH-2011-LDS", "Pilizar_-2020-bachelor", "pirch_2021_VRN", "pointner_simon-2020", "polatsek-2018-stv", "Presch_2020", "Priselac2021", "Purchase-2020-gd", "Purgathofer-2017-China1", "Purgathofer-2017-China2", "Purgathofer-2017-VC-Interface", "raidou_2019_pelvisrunner", "raidou_2019_preha", "raidou_2019_springer", "raidou_bestphd", "raidou_EuroVis15", "raidou_eurovis16", "raidou_miccai16", "raidou_pingu2020", "raidou_previs2021", "raidou_vcbm14", "raidou_vis14", "raidou_vis15", "raidou_visgap2020", "raidou2019_prsps", "Ribicic_2012_VAS", "Rippberger_2019", "sakr_sherif-2020-cacm", "samoul-2019-cnp", "sbardellati-2021-eveos", "schmidt-phd", "sietzen-2019-wnn", "sietzen-2021-perturber", "sietzen-2022-vacnnr", "sietzen-ifv-2019", "sikachev_peter-2011-protovis", "smiech-2018-tei", "sMolBoxes_2022", "Sorger_2013_nMI", "sorger-2013-neuromap", "sorger-2015-litevis", "sorger-2015-taxintec", "sorger-2017-thesis", "sorger-2019-odn", "sorger-2021-egonet", "spegel-gruenberger-2020", "steinboeck-2017-vbn", "steinboeck-2017-vefp", "steinboeck-2018-lbg", "STEINLECHNER-2019-ICT", "steinschorn-2025-parameter", "stoff-2025-pvu", "stoff-concepMap-2021", "stritzel2022", "Strohmayer-2018-BT", "takahashi-2019-acdt", "tekaya-2025-amo", "Thomas-2018", "TR1862162", "Troidl_2019", "turner-aso", "unger-2019_vcp", "vad_viktor-2017-WVE", "vaico", "Vasileva-2018", "vasileva-2019-smw", "Wagner_032017", "wagner-2024-par", "walchhofer_ar_flowmaps", "waldin-2019-ccm", "waldner-2013-facetCloudsGI", "waldner-2013-ubiWM", "waldner-2014-ghi", "waldner-2017-vph", "waldner-2019-rld", "waldner-2020-tbg", "waldner-2021-leo", "waser_2011_VSD", "webGPU_aggregateVis-2023", "wolf-2023-cc", "wolf-2024-jhd", "wu-2017-dagstuhl", "wu-2018-JVLC", "wu-2018-metabo", "wu-2018-shonan", "wu-2018-story", "wu-2019-bmc", "wu-2019-report", "wu-2019-smw", "wu-2019-smwp", "wu-2019-vcbm", "wu-2019-visworkshop", "wu-2020-eurovis-star", "wu-2020-tvcg", "ymca", "YOGHOURDJIAN2019" ], "projects": [ "BioNetIllustration", "d10206", "d10413", "d10450", "d9282", "d9522", "d9555", "deskollage", "Scenario Pool" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=InfoVis", "__class": "ResearchArea" }, { "id": "NetVis", "drupal_id": 9472, "name": "Network Visualization", "short_abstract": "This research area aims to investigate how to best visualize graph-typed data, both abstractly as well as in domain-specific applications.", "abstract": "

Graph-typed data is ubiquitous, from logistical or transportation networks, through social or correspondence networks, all the way to more abstract biochemical interaction networks. Each such application domain brings with it unique analytical goals, metadata, canonical representations, as well as types of vertices and edges. Routine, automated analyses of such data, while important, can often not fully support the kind of decision-making or data exploration necessary for more complex and/or larger networks. Here, it is through (interactive) visualization that we can support domain experts in their exploratory and (semi-) confirmatory research endeavors. On the one hand, we can build custom, tailor-made interactive, visual dashboards which directly address the needs and support the analysis of our domain expert collaborators. On the other, we can look at more foundational questions about how to best represent graph-typed data or better understand the factors which negatively/positively affect comprehension.

\r\n", "website": "https://www.cg.tuwien.ac.at/research/area/NetVis", "teaser": { "name": "teaser.png", "path": "research_area:NetVis", "type": "image/png", "size": 1948873, "orig_name": "teaser.png", "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/research_area/NetVis/teaser:thumb{{size}}.png", "thumb_image_sizes": [ 64, 120, 300 ] }, "workgroups": [ "vis" ], "publications": [ "ehlers-2023-iro", "ehlers-2024-mmm", "ehlers-2024-vgs", "ehlers-2025-ait", "Klaus2024", "mazurek-2018-veq", "sorger-2019-odn", "sorger-2021-egonet", "steinboeck-2018-lbg", "waldner-2014-ghi", "waldner-2020-tbg" ], "projects": [], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=NetVis", "__class": "ResearchArea" } ], "keywords": [ "compound graph visualization", "literature survey", "group structure visualization" ], "weblinks": [], "files": [], "projects_workgroups": [ { "id": "vis", "drupal_id": 1354, "name": "Visualization Group", "website": "https://www.cg.tuwien.ac.at/group/Visualization-Group", "projects": [ "ADAPT", "AngioVis", "Animated Cell Tab development", "AutARG", "avr", "BioNetIllustration", "COMRADE", "d10027", "d10162", "d10164", "d10206", "d10214", "d10413", "d10450", "d10489", "d4328", "d4335", "d4534", "d9275", "d9282", "d9522", "d9555", "d9671", "d9951", "deskollage", "diagvis", "exvisation", "Illustrare", "illvisation", "KASI", "ManyViews", "NeuroViewer", "ProcessIllVis", "ProKeyAnim", "PVG", "Scale-VS", "Scenario Pool", "Semantic Steering", "Sim-CT", "Smart-CT", "Smile Analytics", "ViMaL", "VISAR" ], "research_areas": [ "BioVis", "IllVis", "InfoVis", "MedVis", "NetVis", "Perception", "VisMat", "VR" ], "publications": [ "2013_Viola_Ivan_2013_MTS", "2015-image-coll", "2017-kouril-illcell", "2019-ic", "AA2020", "aigner-2025-vhv", "alacruzECR2004", "alacruzVisSym2004", "Alharbi_2021", "Alsallakh_2012_RCW", "alsallakh-2009-iva", "altendorfer-2008-vts", "amabili2021", "amirkhanov_2012", "amirkhanov_2012heur", "amirkhanov-2011", "amirkhanov-2014-ama", "amirkhanov-2018-withteeth", "amirkhanov-2021-diss", "amirkhanov2010AMA", "ammer-2008-lst", "An-2018-CHI", "AndreUrsits_2013_APP", "antes-2023-vhv", "Antonini2020", "arbesser_ba_2023", "Artner-2004-Hig", "Artner-2005-Hig", "Artner-2005-Thesis", "audioflow", "Auzinger_Mistelbauer_2013_CSR", "balabanian-2007-ant", "balabanian-2008-hvv", "Balabanian-2010-IIV", "Balabanian-2010-PSDSV", "Balduz_01", "Basch_2011_ATA", "Bauer_David_2018", "Bauer_Johannes_2014_IWB", "bauer-2015-cbfd", "Bayat_2019", "bayer-2015-gapaot", "bbeer-2013", "Bechtold2020", "Beer_2011_DVE", "Beham_Michael_PSC", "beham-2014-cupid", "bernhard-2006-dvrcta", "bernhard-2014-GTOM", "bernhard-2016-gft", "Bernold_2019", "bernold-2019-epr", "bhore-2021-issac", "birkeland_aasmund_2014_pums", "Birkeland-2012-IMC", "BirsakHanzl-2009", "boeroendy-2022-uio", "boesch_2014_browserHistoryVis", "borgo-2013-gly", "borondy2022", "bouzari-thesis", "bramberger-3pv", "Brandorff-08-OV", "brandorff-2009-erv", "brandstaetter_01", "brandstaetter-2007-vosd", "Brandstaetter2020", "Braunsperger-2025_bsc", "breitenecker-2020-bct", "bront-2023-svo", "brucker-2010-RES", "Bruckner_Stefan_2015_VAS", "bruckner-2004-EVV", "bruckner-2005-AIV", "bruckner-2005-ATI", "bruckner-2005-ICV", "bruckner-2005-vid", "bruckner-2005-VIS", "bruckner-2006-EVV", "bruckner-2006-ICE", "bruckner-2007-EDF", "bruckner-2007-STF", "bruckner-2008-IIV", "bruckner-2008-IVV", "bruckner-2009-BVQ", "bruckner-2009-IVV", "bruckner-2010-HVC", "bruckner-2010-IFC", "bruckner-2010-ISM", "bruckner-2010-SES", "Bruckner-2012-VEA", "buerger_raphael_2006_3dpoly", "Buerger-2007-cmv", "Buerger-2007-eurovis", "buerger-2007-star", "bugnar-esk-2021", "burns-2007-fea", "bydlinski-2015-gapaot", "byska-2019-mdfc", "Cai_2018", "Caic2021", "Carata_2011_IVE", "carbesser-2013", "casellato-2016-pkg", "cellVIEW_2015", "cizmic-2018-evd", "cmolik-2020-tvcg", "Cornel_2020", "Cornel_Daniel_2015_VOC", "Cornel2016CFM", "coto-2005-MAC", "Cruz-thesis", "Csebfalvi-2002-FPVF", "Csebfalvi-2002-SBICG", "Csebfalvi-2002-Smo", "Csebfalvi-2012-IOM", "Csuk_2011", "daae-lampe-2010-dif", "dataset-christmastree", "dataset-present", "dataset-stagbeetle", "Deutsch-2018", "Dhanoa_2021", "dhanoa-2025-ave", "Dhanoa2024", "Diehl_2015", "Diehl_2021", "Diehl-2017-Albero", "Dietrich_1", "dietrich-2016-viseq2", "diss-thesis-bratislava", "diss-thesis-magdeburg", "diss-thesis-siegen", "djuric-2020-wvo", "donabauer_2019_1", "donabauer-2015-asc", "draeger-2005-chain", "dworschak-2016-szcm", "dworschak-2016-szcm", "dworschak-2024-aml", "Eckelt_2017", "Eckelt_2018_01", "Edi_2019_10_01", "edlinger-2018-vwr", "eg-tut2005-iv", "Ehlers_PhD", "ehlers-2023-iro", "ehlers-2024-mmm", "ehlers-2024-vgs", "ehlers-2025-ait", "Eichner2022", "eitler-2024-sos", "eiweck-hnv-2021", "ellhotka-2025-ipo", "emsenhuber-2008-vhd", "erler-2025-lidarscout", "eschner-2023-evl", "eschner-2023-ims", "eschner-2025-ide", "eschner-blur-2022", "Escribano_2017", "Essler2022", "EuroVis2018-ShortPapers-Karall", "EuroVis2018-ShortPapers-Reiter", "facial2004", "Fechter_2011_DBS", "filipovic-2014-dect", "FISCHL-2012-CTASEG", "fmistelbauer-2014-ad", "fmistelbauer-2014-adict", "Ford-2012-HRV", "fourousan2021", "freiler-2008-ste", "fritz-2009-ieq", "Froehler_Berhnard_2015_ESM", "froschauer-2009-iod", "Fruehstueck_Anna_2015_DOM", "fuchs_raphael_2007_par", "fuchs_vhml", "fuchs-2008-del", "fuchs-vortex", "furmanova_2020", "gadllah_2016", "Gadllah_Hani_2016", "gall_2016", "Gall_2017", "gall_alexander_2016BA", "gall-2025-marv", "Gall2020", "Gall2021", "gangl-2018-gpsart", "Ganglberger-2022-cg", "ganglberger2019", "Ganuza", "Ganuza_2015", "Ganuza_MJ_2015_AVe", "Ganuza_ML_2015_ISA", "Ganuza-2017-IVCSGM", "Gavrilescu_2011_VGP", "gavrilescu-2008-vsc", "gavrilescu-2009-postrend", "Gavrilescu-2010-CIE", "Gehrer_Daniel_CUI", "Gehrer-2017", "Gehrer-2017-molmach", "georgiev-2019-cbg", "gerl-2006-vhi", "geymayer-2017-std", "Gietl_2020", "Gillmann2021", "glanznig-2009-LAMC", "glinzner-2016-tex", "goel-2024-rdr", "Gogel2020", "gostler-2015-3dp", "grammatikaki-2025-htr", "grammatikaki-thesis", "grimm-2004-arefined", "GRIMM-2004-FDMX-P", "grimm-2004-memory", "GRIMM-2004-PPC", "grimm-2004-volume", "Groeller_2011_CVC", "Groeller_2011_CW", "Groeller_2011_GBC", "Groeller_2011_HP", "Groeller_2011_HSU", "Groeller_2011_IPV", "Groeller_2011_IVP", "Groeller_2011_NR", "Groeller_2011_NSM", "Groeller_2011_VCD", "Groeller_2011_VSD", "Groeller_2011_VSD", "Groeller_2011_VSD", "Groeller_2011_VSS", "Groeller_2012_KAV", "Groeller_2012_VBP", "Groeller_2012_ViV", "Groeller_2012_VV", "Groeller_2014_CaQ", "Groeller_2014_CQV", "Groeller_2014_CV", "Groeller_2014_RWA", "Groeller_2014_STC", "Groeller_2014_UPS", "Groeller_2014_VND", "Groeller_2016_I2", "Groeller_2016_I7", "Groeller_2016_I8", "Groeller_2016_P2", "Groeller_2016_P4", "Groeller_2016_P6", "Groeller_2016_P7", "Groeller_2021_11", "Groeller_2021_BVM", "Groeller_2021-10", "Groeller_Edi_2013_TVC", "Groeller_Edi_2013_VCQ", "Groeller_Edi_2013TVCK", "Groeller_Edi_VKA", "Groeller_Eduard_2014_THS", "Groeller_Eduard_2014_TSP", "Groeller_Eduard_2014_UCT", "Groeller_Eduard_2015_SMV", "Groeller_Eduard_2015_TCU", "Groeller_V1_2020", "Groeller_V2_2020", "Groeller_V3_2020", "Groeller_V42020", "Groeller_V5_2020", "groeller-2004-efficient", "groeller-2004-imp", "groeller-2004-sci", "groeller-2005-dia", "Groeller-2010-CVC", "Groeller-2010-IFI", "Groeller-2010-IVVI", "Groeller-2011-HSH", "Groeller-2017-ARTORW", "Groeller-2017-AVC", "Groeller-2017-IVAMCS", "Groeller-2017-PTPMD", "Groller_2021-12-03", "Gronde_2008", "Grorller-2010-IVV", "grossmann_2019_pelvisrunner_poster", "Grossmann_MA", "grossmann-2021-layout", "grossmann-2022-conceptSplatters", "grossmann-thesis", "gruber-2025-vps", "Gruenauer-2011-CAT", "Gschwantner_2011_AMQ", "gundacker-2020-wlm", "Gurbat_2011_SBS", "gusenbauer-2018", "gusenbauer-2018", "gusenbauer-2018-P", "gutekunst_2019", "Hadwiger-2002-Fas", "haidacher_2010_statTF", "haidacher-2004-FVV", "haidacher-2005-MND", "haidacher-2007-idr", "haidacher-2008-vcbm", "haidacher-2011-MSM", "haidacher-2011-phd", "haidacher-2011-VAM", "Hainoun2019", "Halladjian_2020", "hanzl-2011", "hartl-2008-vcd", "hartmann-2005-adv", "haushofer-2018-skinning", "Heim_2020", "Heim_2021", "Heim-2017", "heim-2024-accustripes", "heim-gall-2020-dde", "heinzl_2006_RSDVCDM", "heinzl-2006-rep", "heinzl-2008-sam", "Heinzl2021", "Herghelegiu-2012-BPV", "herzberger-2023-swv", "herzberger-2024-roh", "Hirsch_Christian_2015_ABL", "Hladuvka-2002-Exp", "hladuvka-2002-exploiting", "Hochmayr_Manuel_2015_PSE", "hochmayr_manuel-2011-VoRMD", "Hochpoechler_2016_VR", "horvath-2018-ism", "hromniak-2019-vcn", "huber-2025-eed", "Iijima-2020-iV", "indirectBiasLanguageModels-2023", "irendorfer-2024-uat", "irger-2023-ves", "Ismail-2018", "jank-2022-cbf", "Kanitsar-2002-CPR", "Kanitsar-2003-Adva", "Kanitsar-2003-Dem", "Kanitsar-2004-Dia", "Kanitsar-thesis", "kapferer-2017-godot", "Karall2017CVAB", "Karall2018_2", "karimov-2013-vivisection", "karimov-2015-HD", "karimov-2016-GIVE", "karimov-2016-SD", "keglevic_schulz-2011-MB", "Kehrer-2008-hyp", "Kehrer-2010-mom", "kehrer-2011-vai", "kehrer-2012-mod", "kehrer-2013-IVA", "kehrer-2013-SBC", "Kehrer-2013-STAR", "Kehrer-2014-CSD", "kerbl-2024-ah3", "kimmersdorfer-fancyMaps-2023", "Kinkelin_2009", "Kinkelin-2011-VR", "Klaus2024", "Kleber-2004-TFE", "klein_2017_IM", "klein_2019_MSA_Poster", "klein_2019_PGG", "klein_2019_PHD", "klein_2019_PMP", "Klein_Tobias_2015TIV", "klein-thesis", "Knapp_2017_CVR", "knapp_michael_2004_MAS", "knapp-2004-semi", "koch_bernhard_2018-1", "koch-2018-sso", "koehle-2013-sgv", "koeppel-2021", "Koeppel2020", "Kohlmann-2007-EBV", "kohlmann-2007-livesync", "kohlmann-2008-lse", "kohlmann-2009-cp", "kohlmann-2009-lssl", "kolesar-ivan-2014-polymers", "Kolter_2021", "komon-2025-dco", "komon-2025-webigeo", "komon-2025-webigeo", "komon-kimmersdorfer-2025-weBIGeo", "Konyha_2013_IVA", "korpitsch_thorsten-2019", "korpitsch-2020-cescg", "Korpitsch-2020-wscg", "korpitsch-2021-lov", "Koszticsak-2017-ewt", "Kouril_2017_11", "kouril-2015-maya2cellview", "kouril-2017-sccgposter", "kouril-2018-LoL", "kouril-2018-LoL", "kouril-2020-hyperlabels", "kouril-2021-molecumentary", "kouril-2021-phdthesis", "Kovacs_PhD", "krakhofer-2015", "Krone2016VABC", "kuehschelm-2005-adv", "Kugler_2019", "Kuroko-2020-iV", "Labschuetz_Matthias_2015_JIT", "Labschuetz_Matthias_2016_AHD", "Labschuetz_Matthias_2016_JIT", "Labschuetz_Matthias_2016_JITT", "Lackner_2022", "LaCruz2004", "Ladstaedter-2009-sim", "Langer_Edith_IR1", "Langer_Maximillian_DMV", "Laschober-2018", "lawonn-2018-illvisstar", "lemuzic_2014_ipv", "lemuzic_2015_timelapse", "LeMuzic_2016_PhD", "lemuzic-2014-ivm", "lemuzic-2015-hiv", "lemuzic-mindek-2016-viseq", "lemuzic-mindek-sorger-cgfcc", "Leutschacher_2022", "li-2019-gdc", "lie-2009-cri", "lipp-2017-mgpu", "lipp-2017-vulkan", "lippeck-2022-mna", "liu-2014-ac", "lkoell-2014", "ludwig-2012-MT", "Machegger2018DTI", "magg-2021-dva", "magg2021", "mahler-2021-mdar", "major-2010-mrf", "malan_fluoro", "malik-2007-FP", "malik-2009-CVFA", "malik-thesis", "Manczarski-2016", "Marek06", "Maricic_2013_VFE", "martorell2022", "maruyama-2019-iv", "Masopust_2018", "Masterthesis-Viola", "Matkovic_Kresimir_2015_", "matt-2023-vae", "matt-2024-cvil", "matt-2025-scv", "matt-masterthesis", "Mattausch-2003-ISL", "Mattausch-2003-SIE", "Mattausch-2003-Str", "Matusich-2017-01", "Mautner 2020", "Mayr-2017-1", "mayr-2021-wec", "mazurek-2017-sio", "mazurek-2017-vows", "mazurek-2018-vac", "mazurek-2018-veq", "Mazza_2020", "Mazza_2021_05", "meka-2024-lpi", "Melo_2018_11", "Meusburger_2020", "Miao_2015_VCBM", "Miao_2019", "Miao_Haichao_2015_VQC", "miao_inria_2017", "miao_kaust_2018", "miao_nantech_2019", "miao_nar_2020", "miao_plitvice_2018", "miao_thesis_2019", "miao_tvcg_2017", "miao_tvcg_2018", "miao_tvcg_2018", "miao_tvcg_2018", "miao2018Dimsum", "Miao2018FDN", "Michl_2017_CSV", "mindek_peter-2012-visgroupproj", 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"__class": "Workgroup" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2024/ehlers-2024-vgs/", "__class": "Publication" }, { "id": "marin-2024-pcp", "type_id": "inproceedings", "tu_id": null, "repositum_id": "20.500.12708/196496", "title": "Parameter-free connectivity for point clouds", "date": "2024-02", "abstract": "Determining connectivity in unstructured point clouds is a long-standing problem that has still not been addressed satisfactorily. In this paper, we analyze an alternative to the often-used k-nearest neighborhood (kNN) graph - the Spheres of Influence Graph (SIG). We show that the edges that are neighboring each vertex are spatially bounded, which allows for fast computation of SIG. Our approach shows a better encoding of the ground truth connectivity compared to the kNN for a wide range of k, and additionally, it is parameter-free. Our result for this fundamental task offers potential for many applications relying on kNN, e.g., parameter-free normal estimation, and consequently, surface reconstruction, motion planning, simulations, and many more.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 742, "image_height": 912, "name": "marin-2024-pcp-image.jpg", "type": "image/jpeg", "size": 190057, "path": "Publication:marin-2024-pcp", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/marin-2024-pcp-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/marin-2024-pcp-image:thumb{{size}}.png" }, "sync_repositum_override": "date", "repositum_presentation_id": null, "authors": [ { "id": 1848, "drupal_id": 3143, "orcid_id": "0000-0002-8812-9719", "website": 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"Publication:marin-2024-pcp", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/marin-2024-pcp-image.jpg", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/marin-2024-pcp-image:thumb{{size}}.png" }, { "description": null, "filetitle": "paper", "main_file": true, "use_in_gallery": false, "access": "public", "name": "marin-2024-pcp-paper.pdf", "type": "application/pdf", "size": 2193223, "path": "Publication:marin-2024-pcp", "url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/marin-2024-pcp-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2024/marin-2024-pcp/marin-2024-pcp-paper:thumb{{size}}.png" } ], "projects_workgroups": [ { "id": "Superhumans", "workgroup_id": "rend", "drupal_id": 4166, "drupal_path": "/research/projects/Superhumans", "name": "Superhumans - Walking Through Walls", "name_de": null, "short_title": "Superhumans", "website": null, "status": "active", "short_abstract": "

In recent years, virtual and augmented reality have gained widespread attention because of newly developed head-mounted displays. For the first time, mass-market penetration seems plausible. Also, range sensors are on the verge of being integrated into smartphones, evidenced by prototypes such as the Google Tango device, making ubiquitous on-line acquisition of 3D data a possibility. The combination of these two technologies – displays and sensors – promises applications where users can directly be immersed into an experience of 3D data that was just captured live. However, the captured data needs to be processed and structured before being displayed. For example, sensor noise needs to be removed, normals need to be estimated for local surface reconstruction, etc. The challenge is that these operations involve a large amount of data, and in order to ensure a lag-free user experience, they need to be performed in real time, i.e., in just a few milliseconds per frame. In this proposal, we exploit the fact that dynamic point clouds captured in real time are often only relevant for display and interaction in the current frame and inside the current view frustum. In particular, we propose a new view-dependent data structure that permits efficient connectivity creation and traversal of unstructured data, which will speed up surface recovery, e.g. for collision detection. Classifying occlusions comes at no extra cost, which will allow quick access to occluded layers in the current view. This enables new methods to explore and manipulate dynamic 3D scenes, overcoming interaction methods that rely on physics-based metaphors like walking or flying, lifting interaction with 3D environments to a “superhuman” level.

\r\n", "abstract": "

In recent years, virtual and augmented reality have gained widespread attention because of newly developed head-mounted displays. For the first time, mass-market penetration seems plausible. Also, range sensors are on the verge of being integrated into smartphones, evidenced by prototypes such as the Google Tango device, making ubiquitous on-line acquisition of 3D data a possibility. The combination of these two technologies – displays and sensors – promises applications where users can directly be immersed into an experience of 3D data that was just captured live. However, the captured data needs to be processed and structured before being displayed. For example, sensor noise needs to be removed, normals need to be estimated for local surface reconstruction, etc. The challenge is that these operations involve a large amount of data, and in order to ensure a lag-free user experience, they need to be performed in real time, i.e., in just a few milliseconds per frame. In this proposal, we exploit the fact that dynamic point clouds captured in real time are often only relevant for display and interaction in the current frame and inside the current view frustum. In particular, we propose a new view-dependent data structure that permits efficient connectivity creation and traversal of unstructured data, which will speed up surface recovery, e.g. for collision detection. Classifying occlusions comes at no extra cost, which will allow quick access to occluded layers in the current view. This enables new methods to explore and manipulate dynamic 3D scenes, overcoming interaction methods that rely on physics-based metaphors like walking or flying, lifting interaction with 3D environments to a “superhuman” level.

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Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

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In this poster, we propose an extension to the proximity graph introduced in [MOW22] to three-dimensional models. We use the spheres-of-influence (SIG) proximity graph restricted to the 3D Delaunay graph to compute connectivity between points. Our approach shows a better encoding of the connectivity in relation to the ground truth than the k-nearest neighborhood (kNN) for a wide range of k values, and additionally, it is parameter-free. Our result for this fundamental task offers potential for many applications relying on kNN, e.g., improvements in normal estimation, surface reconstruction, motion planning, simulations, and many more.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "image", "main_file": false, "use_in_gallery": true, "access": "hidden", "image_width": 455, "image_height": 200, "name": "marin-2023-pic-image.png", "type": "image/png", "size": 20480, "path": "Publication:marin-2023-pic", "url": "https://www.cg.tuwien.ac.at/research/publications/2023/marin-2023-pic/marin-2023-pic-image.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/marin-2023-pic/marin-2023-pic-image:thumb{{size}}.png" }, "sync_repositum_override": "booktitle", "repositum_presentation_id": null, "authors": [ { "id": 1848, "drupal_id": 3143, "orcid_id": "0000-0002-8812-9719", "website": 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"url": "https://www.cg.tuwien.ac.at/research/publications/2023/marin-2023-pic/marin-2023-pic-paper.pdf", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2023/marin-2023-pic/marin-2023-pic-paper:thumb{{size}}.png" } }, "projects_workgroups": [ { "id": "WorldScale", "workgroup_id": "rend", "drupal_id": 4158, "drupal_path": "/research/projects/WorldScale", "name": "Modeling the World at Scale", "name_de": "Modellierung der Welt nach Maß", "short_title": "WorldScale", "website": null, "status": "active", "short_abstract": "Vision: reconstruct a model of the world that permits online level-of-detail extraction.", "abstract": "

Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

\r\n", "start_date": "2020-05-01", "end_date": "2026-04-30", "leader_id": 948, "logo": { "name": "logo.jpg", "path": "project:WorldScale", "type": "image/jpeg", "size": 64953, "orig_name": "logo.jpg", "thumb_url": "https://www.cg.tuwien.ac.at/research/projects/WorldScale/logo:thumb{{size}}.png", "thumb_image_sizes": [ 120, 300 ] }, "funding_organisations": [ { "id": "WWTF", "contract_number": "ICT19-009", "comment": "€ 578.450" } ], "research_areas": [ "Geometry" ], "publications": [ "antes-2026-maa", "ausserer-2026-baa", "balvan-rsf", "beleznai_csaba-2025-pr", "belic_dejan_pr", "bodzenta-2026-baa", "csecsy-fcd", "darovskikh_leonid-2025-prj", "deak-a3", "depner_dennis-2020-baa", "depner_dennis-2022-maa", "elagrod_ahmed-baa", "fitzinger_andreas-daa", "fraissl_niklas-2023-pra", "fuchs_peter-2024-baa", "fuericht_2025_prj", "fuericht_elias-2025-prj", "hamzic_ademir-baa", "harisch-mvs", "hatzinger-2026-baa", "hoffer-toth-com", "holom_2025-prj", "jira_marcel-2024-baa", "kaipel_nikolas-2025-baa", "karampekios_anastasios-prj", "kazda-tmd", "knoetzl_alexander-2025-prj", "kohlhas_jakob-2023-pra", "konrad-2020-baa", "kozonits_marcel-2025-baa", "kralicek_sebastian_baa", "kubicek-2024-baa", "lang_jakob-2022-baa", "lazarevic-tci", "leutschacher_2025_prj", "macho_jonas-2024-baa", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "markovich_aleksandar-2022-maa", "mayr_patrick_maa", "micu_2025-prj", "miklautsch_florian-baa", "mueller-2024-rbt", "mujadzic_iman_baa", "mujadzic-2023-hfp", "nawrocki-2024-nsr", "negi_ashish-2023-baa", "neumann-sfd", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "pakkanen-2026-prj", "parakkat-2024-ballmerge", "pointner_simon-2022-maa", "proelss-bdp", "prohaska_julia-2023-baa", "reiher_joerg-2023-pra", "reiher-joerg-2024-daa", "rozanics-lsf", "rubik_michael-2022-baa", "schrammel_sebastian_baa", "schulz-faf", "seisenbacher-cyclesafely", "slajcho_levente-baa", "slavtchev-dcd", "starnberger-cli", "steinheber-2024-oto", "steinschauer-2020-baa", "trenovatz-lpd", "tumpach_barbara-pra", "vasiljevic-gpc", "wiesinger_2025-baa", "wiesinger_klemens-2023-baa", "wildt-tci", "withalm_brigitte-2022-baa", "wolffhardt-2026-prj", "yoo-chaeran-2025-pr", "zeilinger_matthias-2020-baa" ], "url": "https://www.cg.tuwien.ac.at/research/projects/WorldScale", "__class": "Project" } ], "url": "https://www.cg.tuwien.ac.at/research/publications/2023/marin-2023-pic/", "__class": "Publication" }, { "id": "marin-2022-sigdt", "type_id": "journalpaper", "tu_id": null, "repositum_id": "20.500.12708/175713", "title": " SIGDT: 2D Curve Reconstruction", "date": "2022-10-05", "abstract": "Determining connectivity between points and reconstructing their shape boundaries are long-standing problems in computer graphics. One possible approach to solve these problems is to use a proximity graph. We propose a new proximity graph computed by intersecting the to-date rarely used proximity-based graph called spheres-of-influence graph (SIG) with the Delaunay triangulation (DT). We prove that the resulting graph, which we name SIGDT, contains the piece-wise linear reconstruction for a set of unstructured points in the plane for a sampling condition superseding current bounds and capturing well practical point sets' properties. As an application, we apply a dual of boundary adjustment steps from the CONNECT2D algorithm to remove the redundant edges. We show that the resulting algorithm SIG-CONNECT2D yields the best reconstruction accuracy compared to state-of-the-art algorithms from a recent comprehensive benchmark, and the method offers the potential for further improvements, e.g., for surface reconstruction.", "authors_et_al": false, "substitute": null, "main_image": { "description": null, "filetitle": "Teaser", "main_file": false, "use_in_gallery": true, "access": "public", "image_width": 7066, "image_height": 3125, "name": "marin-2022-sigdt-Teaser.png", "type": "image/png", "size": 457323, "path": "Publication:marin-2022-sigdt", "url": "https://www.cg.tuwien.ac.at/research/publications/2022/marin-2022-sigdt/marin-2022-sigdt-Teaser.png", "thumb_image_sizes": [ 16, 64, 100, 175, 300, 600 ], "thumb_url": "https://www.cg.tuwien.ac.at/research/publications/2022/marin-2022-sigdt/marin-2022-sigdt-Teaser:thumb{{size}}.png" }, "sync_repositum_override": "issn", "repositum_presentation_id": null, "authors": [ { "id": 1848, "drupal_id": 3143, "orcid_id": "0000-0002-8812-9719", "website": "https://www.cg.tuwien.ac.at/staff/DianaMarin", "firstname": "Diana", "lastname": "Marin", "searchname": "Diana Marin", "is_teacher": false, "name": "Diana Marin", "publications": [ "ehlers-2024-vgs", "marin-2022-sig", "marin-2022-sigdt", "marin-2023-pic", "marin-2024-dsr", "marin-2024-pcp", "marin-2024-rcf", "marin-2024-sing", "marin-thesis", "ohrhallinger_stefan-2024-inv" ], "publications_first": [], "project_leader": [], "publication_field": [ { "publ_id": "brunner_lukas_2022_ssb", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "Komon2022", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2022-sig", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2022-sigdt", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2023-pic", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2024-dsr", "field_id": "lecturer", "ordernum": 0 }, { "publ_id": "marin-2024-pcp", "field_id": "lecturer", 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"ohrhallinger_stefan-2018-pg", "ohrhallinger_stefan-2019-xms", "ohrhallinger_stefan-2022-it1", "ohrhallinger_stefan-2022-tut", "ohrhallinger_stefan-2023-con", "ohrhallinger_stefan-2024-inv", "ohrhallinger-2016-sgp", "ohrhallinger-2021-egs", "ohrhallinger-2022-e2t", "ohrhallinger-2025-tsd", "ohrhallinger-stefan-2012-the", "parakkat-2024-ballmerge", "Radwan_2021_Occ", "Radwan-2014-CDR", "Radwan-2017-Occ", "SCHUETZ-2020-MPC" ], "publications_first": [], "project_leader": [ "d10381", "Superhumans", "WorldScale" ], "publication_field": [ { "publ_id": "antes-2026-maa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "ausserer-2026-baa", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "balvan-rsf", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "beleznai_csaba-2025-pr", "field_id": "co_supervisor", "ordernum": 0 }, { "publ_id": "belic_dejan_pr", "field_id": "supervisor", "ordernum": 0 }, { "publ_id": "bodzenta-2026-baa", "field_id": "co_supervisor", 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Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

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The algorithm exploits the to-date rarely used proximity-based graph called spheres-of-influence graph (SIG). We filter edges from the Delaunay triangulation belonging to the SIG as an initial graph and apply some additional processing plus elements from the Connect2D algorithm. 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Example applications are collision detection, reconstruction, compression, occlusion-aware surface handling and improved sampling conditions.", "abstract": "We perform research in surface reconstruction from point clouds, geometry processing and geometry representations. We use parallel processing of thousands of GPU cores to enable flash geometry operations in real-time.\nNovel data structures are evaluated to reduce the time complexity of geometric operations, such as collision detection or occlusion-aware surface processing.\nIn computational geometry, we prove concepts relating samples to curves and manifolds. We also research novel surface description paradigms and according operators, representing large point cloud data in a probabilistic way. 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"tumpach_barbara-pra", "unterguggenberger-2021-msh", "Vucenovic_2022", "WEYDEMANN-2016-NUO", "weydemann-2025-noo", "wiesinger_2025-baa", "wiesinger_klemens-2023-baa", "Wimmer-1998-SMO", "WIMMER-2006-DWN", "WIMMER-2006-IP", "WIMMER-2016-HARVEST4D", "withalm_brigitte-2022-baa", "wolffhardt-2026-prj", "yoo-chaeran-2025-pr", "zeilinger_matthias-2020-baa", "Zezulka-2024-FTS" ], "projects": [ "3DSpatialization", "d4388", "d9275", "EVOCATION", "GameTools", "Harvest4D", "SCI_BIM", "ShapeAcquisition", "Superhumans", "TERAPOINTS", "WorldScale" ], "url": "https://www.cg.tuwien.ac.at/research/publications/show.php?class=ResearchArea&id=Geometry", "__class": "ResearchArea" } ], "keywords": [ "Curve reconstruction", "Spheres-of-influence graph", "Computing methodologies", "Point-based models" ], "weblinks": [ { "href": "https://diglib.eg.org/handle/10.2312/egp20221013", "caption": "Paper", "description": null, "main_file": 1 }, { "href": "https://www.youtube.com/watch?v=JExKRTSI0Po", "caption": 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"name_de": "Modellierung der Welt nach Maß", "short_title": "WorldScale", "website": null, "status": "active", "short_abstract": "Vision: reconstruct a model of the world that permits online level-of-detail extraction.", "abstract": "

Vision: reconstruct a model of the world that permits online level-of-detail extraction. The core idea in this project is to interactively integrate sensed 3D data of varying sources and scales into a topologically clean surface. Our varying-scale model then permits online extraction of seamless levels of detail for rendering with minimal aliasing and popping artifacts. For this, we develop new topological guarantees to minimize the needed geometry. By exploiting the inherent redundancy of 2D surfaces in 3D, we design a fast way to robustly detect changes that let users better control the scan acquisition process. The topologically clean output surface and the change detection permit easy processing of the geometry for common use cases such as autonomous navigation, environment learning, augmented reality displays of georeferenced semantic information. An example application is fusing and distributing scans from the built-in sensors of multiple autonomous vehicles (ground, air), for incidental map updating as well as guaranteed efficient collision detection and tracking changes for path planning.

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